Set Of Hubs Research Articles

A Study on Consistency and Its Actual Situation of Hub Setting by Urban Facility Location Plan and Regional Public Transportation Plan in Local City

A Study on Consistency and Its Actual Situation of Hub Setting by Urban Facility Location Plan and Regional Public Transportation Plan in Local City

Integrating brainstem and cortical functional architectures.

The brainstem is a fundamental component of the central nervous system, yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging to derive a functional connectome encompassing cortex and 58 brainstem nuclei spanning the midbrain, pons and medulla. We identified a compact set of integrative hubs in the brainstem with widespread connectivity with cerebral cortex. Patterns of connectivity between brainstem and cerebral cortex manifest as neurophysiological oscillatory rhythms, patterns of cognitive functional specialization and the unimodal-transmodal functional hierarchy. This persistent alignment between cortical functional topographies and brainstem nuclei is shaped by the spatial arrangement of multiple neurotransmitter receptors and transporters. We replicated all findings using 3-Tesla data from the same participants. Collectively, this work demonstrates that multiple organizational features of cortical activity can be traced back to the brainstem.

Analysis of hub parameters in fuzzy hypergraphs extending to intuitionistic fuzzy threshold hypergraphs: Applications in designing transport networks in amusement parks using hub hyperpaths

A hypergraph is a generalization of a graph where an edge can connect any number of vertices. In this paper, many different aspects of fuzzy hypergraphs and their applications are examined. The concepts of the hub hyperpath, hub set, and hub number of fuzzy hypergraphs are defined and analyzed. Specifically, the hub number in complete fuzzy hypergraphs has been analyzed and provided an example as an application part in transportation systems. Furthermore, these concepts have been expanded to Intuitionistic Fuzzy Hypergraphs (IFHGs), defining the hub hyperpath, hub set, and hub number. Additionally, the problem of designing an efficient electric transport system for moving between game stations in a large amusement park has been investigated using hub analysis within the framework of an Intuitionistic Fuzzy Threshold Hypergraph (IFTHG) environment.

Hub Cover Pebbling Number

The hub cover pebbling number, h ∗ ( G ) , of a graph $G$, is the least non-negative integer such that from all distributions of h ∗ ( G ) pebbles over the vertices of G , it is possible to place at least one pebble each on every vertex of a set of vertices of a hub set for G using a sequence of pebbling move operations, each pebbling move operation removes two pebbles from a vertex and places one pebble on an adjacent vertex. Here we compute the hub cover pebbling number for wheel related graphs.

What Are the Perspectives and Experiences of Older Adults of Existing Integrated Care Services in the Mid-West Region of Ireland?

Abstract Background Substantial investment has been provided by the HSE in the form of The Integrated Care Programme for Older People (ICPOP) Community Specialist Teams (CSTs) for older people. These teams aim to provide timely specialist multi-disciplinary assessment and intervention to help maintain older adults’ independence and ability to live well at home. Little is known about older adult’s views or experiences of this service. This study aimed to resolve this research gap by exploring older adults’ experiences of CSTs within the Irish Healthcare context. Methods A qualitative descriptive research design was employed to explore this research area and a semi structured topic guide was developed. Purposive non-probability sampling was used to recruit 13 older adults who had completed intervention with the CSTs. Individual semi-structured interviews were completed with each participant and were transcribed verbatim. A reflexive approach to thematic analysis was used to guide analysis of the generated data. Results Four themes were identified, older people had limited knowledge of the service and experienced accessibility issues in attending CST within a HUB setting (Theme 1). Older people experienced positive and co-ordinated care by CST (Theme 2). Older people described that they valued the self-management education and support from attending CST that enabled to manage their long-term conditions effectively and maintain their independence (Theme 3). Once discharged from CST older people described positive emotional and physiological impact of CST that enabled them to be more confident to manage daily life at home (Theme 4). Conclusion It was evident from the data that CST had a positive impact on supporting older people to remain independent at home and increased their confidence in self- management of their long-term conditions. Further research would be beneficial to address ways to improve the delivery of care for the complex needs older persons within CSTs as they describe accessibility issues in attending the service.

Influential simplices mining via simplicial convolutional networks

The identification of influential simplices is crucial for understanding higher-order network dynamics. Yet, despite relatively mature research on influential nodes (0-simplices) mining, characterizing simplices’ influence and identifying influential simplices remain challenging due to notable discrepancies in vital nodes and vital simplices mining. In this paper, we propose a higher-order graph learning model, named influential simplices mining neural networks (ISMnet), to identify vital simplices in simplicial complexes. ISMnet leverages novel higher-order representations: hierarchical bipartite graphs and higher-order hierarchical (HoH) Laplacians, where target simplices are grouped into a hub set and can interact with other simplices. It also employs learnable graph convolution operators in each HoH Laplacian domain to capture interactions among simplices and can identify influential simplices of arbitrary order by changing the hub set. Notably, ISMnet addresses the limitations inherent in traditional graph neural networks that struggle with higher-order tasks, while seamlessly retaining the capability to exploit network topology and node features concurrently. Numerical results on empirical and synthetic datasets demonstrate that ISMnet significantly outperforms existing methods by at least 12% and 4%, respectively, in ranking 2-simplices. In general, this novel framework promises to serve as a potent tool in higher-order network analysis.

Abstract 244: Patient-derived organoids: A pre-clinical model to assess infection and killing capacity of viral vectors for cancer therapy

Abstract HUB’s patient-derived organoids (PDOs) are in vitro models that preserve tissue morphology and original cellular complexity. Additionally, PDOs are compatible with (high) throughput screening methods, enabling informed decision-making early in the drug development pipeline and significantly improving the odds of positive outcomes in clinical trials. Akamis Bio has developed experimental viral vector-based tumour gene therapies that have the potential to treat a variety of solid tumours by driving the expression of immunologically active biomolecules and therapeutic proteins hampering tumour immune evasion. These vectors are based on a replication competent, chimeric group B adenovirus backbone that selectively replicates and expresses transgene payloads in tumor cells, and additionally exerts oncolytic activity. To evaluate the efficacy of such a virus-based gene delivery strategy, HUB set up a proof-of-concept study to assess the propagation capability of Akamis Bio’s viral vectors in tumour PDOs. Intact PDOs from primary pancreatic tumours were infected with several viral loads, ranging from 0.1 viral particles per cell (PPC) to 100 PPC. The viral vector encoded a GFP reporter gene to identify infected cells using fluorescence imaging. The cultures were imaged daily for 14 days, allowing the assessment of cytotoxicity and infection efficiency and dynamics. Independently of PPC, infections always started in the exterior of PDOs in a few GFP-positive cells. The gradual increase over time in GFP-signal toward the interior of PDOs suggests the high likelihood of secondary infection events. Crucially, GFP intensity was correlated with cell death in PDOs, as evidenced by morphological features detected in brightfield images. Moreover, the infection dynamics were dose-dependent, as the GFP intensity peak arose later with decreasing PPC. These results show the capacity of HUB PDOs to study the infection dynamics of Akamis viral vectors. This pre-clinical platform permits rapid and efficient screening and dose evaluation for oncolytic virotherapies. Unique access to extensive tumour PDO biobanks with characterized genomic landscapes allows best-in-class stratification of treatment response based on tumour type and genetic heterogeneity. Citation Format: Francisco Morales-Rodriguez, Maria Stella Sasso, Taya Bezhaeva, Samantha Bailey-Bucktrout, Ka Wai Lai, Foteini Gkogkou, Sylvia Boj. Patient-derived organoids: A pre-clinical model to assess infection and killing capacity of viral vectors for cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 244.

Methodology to design and set up a framework to monitor and evaluate a Citizen Science Hub’s performance and impact

Universities around the world are establishing hubs that promote and support citizen science. A key challenge to the long-term sustainability of such hubs is the absence of an objective, transparent, and efficient way to measure their performance and impact. The main reason is that the benefits of citizen science do not linearly follow an input-to-impact intervention logic. In response, we propose a methodology for developing a dedicated monitoring and evaluation framework. All steps from design to implementation are described, accompanied by examples from INCENTIVE, a project where our methodology was tested in real-life settings. The results indicate that our methodology, with minor adaptations, can be used by similar types of hub setting such as living labs, city labs, innovation hubs, science and technology parks, etc.

GLOBAL HOP HUB NUMBER OF A GRAPH

A set S of vertices in G is hop hub set if for each v ∈ V (G) \ S, there exists u ∈ S such that d(u,v) = 2. It is a global hop hub set of G if it is a hop hub set of both G and its complement G. The minimum cardinality of global hop hub set of G, denoted by ghh(G), is called the global hop hub number of G. In this paper, we determine the global ho hub number of some standard graphs. Also upper and lower bounds for ghh(G) are obtained.
 Keywords: Global, Hub number, Hop hub number, Global hub, Global domination num- ber,Global hop number, Global hop hub number, .

Diverse functional interaction driven by control-default network hubs supports creative thinking.

Complex cognitive processes, like creative thinking, rely on interactions among multiple neurocognitive processes to generate effective and innovative behaviors on demand, for which the brain's connector hubs play a crucial role. However, the unique contribution of specific hub sets to creative thinking is unknown. Employing three functional magnetic resonance imaging datasets (total N = 1,911), we demonstrate that connector hub sets are organized in a hierarchical manner based on diversity, with "control-default hubs"-which combine regions from the frontoparietal control and default mode networks-positioned at the apex. Specifically, control-default hubs exhibit the most diverse resting-state connectivity profiles and play the most substantial role in facilitating interactions between regions with dissimilar neurocognitive functions, a phenomenon we refer to as "diverse functional interaction". Critically, we found that the involvement of control-default hubs in facilitating diverse functional interaction robustly relates to creativity, explaining both task-induced functional connectivity changes and individual creative performance. Our findings suggest that control-default hubs drive diverse functional interaction in the brain, enabling complex cognition, including creative thinking. We thus uncover a biologically plausible explanation that further elucidates the widely reported contributions of certain frontoparietal control and default mode network regions in creativity studies.

Distributed Consensus Hierarchical Optimization and Control Method for Integrated Energy System Based on Event-Triggered Mechanism

For integrated energy systems (IES) composed of a set of energy hubs (EHs), a consensus control method is usually adopted to achieve accurate sharing of electrical and thermal composite energies. To solve the communication redundancy problem of the consensus control method, a hierarchical optimization and distributed control scheme based on a dynamic event-triggered mechanism of EHs is proposed to realize stable operation of IES. An economic optimization strategy based on equal increment principle is improved to minimize the operation costs of IES in the second layer. Due to consensus control being integrated into the supply-demand power deviation calculations of EHs, the desired electrical and thermal power trajectories are accurately determined. To improve dynamic response performances in the presence of uncertain disturbances, an event-triggered communication mechanism is designed in the primary layer. The triggering threshold can be adjusted dynamically according to changes of electrical and thermal power outputs, and the redundant communication requirement in the electrical branches is reduced. Considering the coupling characteristics of IES energy networks, a consensus control method is promoted to synchronously track the desired electric and thermal power trajectories of EHs, and the goal of accurate power sharing is achieved. The frequency and pipeline pressure fluctuations are also limited within the allowable range. The economic optimization and coordinated operation of electrical and thermal composite energies in IES are guaranteed by the proposed hierarchical control structure. Additionally, only information from neighboring EHs at the event-triggered time is involved, so the computation simplicity and control performance can be obtained simultaneously. The hardware-in-loop experimental results are conducted to demonstrate the effectiveness of the proposed control strategy.

Machine learning augmented approaches for hub location problems

Hub location problems are widely analyzed in fields of logistic and transportation industry for cost reduction. In this paper, a novel algorithm framework based on machine learning is proposed to improve solution quality of hub location problems for large-scale instances. First, a deep-learning based probabilistic hub-ranker (DLHr) is developed to determine the priority of nodes to be selected as hubs. Next, two node-ranking based approaches DL-CBS and DL-GVNS are developed to augment the DLHr for single allocation hub location problems. DL-CBS is an augment algorithm embedding DLHr-ranking into clustering-based potential hub sets algorithm (CBS) while DL-GVNS embeds DLHr-ranking into general variable neighborhood search (GVNS). The numerical results evidence that DLHr outperforms baselines on the node-ranking task and helps to identify potential hubs. Evaluation on a wide range of experiments shows that DL-CBS and DL-GVNS improve solution quality of single allocation hub location problems compared with vanilla CBS and GVNS, revealing DLHr ranking helps to boost the performance of traditional heuristics.

1 - Prediction of next generation sequencing test failure in lung adenocarcinoma in a genomic laboratory hub setting

1 - Prediction of next generation sequencing test failure in lung adenocarcinoma in a genomic laboratory hub setting

Network Inference Using the Hub Model and Variants

Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the groups formed by individual subjects. In these applications, the network is itself a parameter of a statistical model. Zhao and Weko propose a model-based approach, called the hub model, to infer implicit networks from grouping behavior. The hub model assumes that each member of the group is brought together by a member of the group called the hub. The set of members which can serve as a hub is called the hub set. The hub model belongs to the family of Bernoulli mixture models. Identifiability of Bernoulli mixture model parameters is a notoriously difficult problem. This article proves identifiability of the hub model parameters and estimation consistency under mild conditions. Furthermore, this article generalizes the hub model by introducing a model component that allows hubless groups in which individual nodes spontaneously appear independent of any other individual. We refer to this additional component as the null component. The new model bridges the gap between the hub model and the degenerate case of the mixture model—the Bernoulli product. Identifiability and consistency are also proved for the new model. In addition, a penalized likelihood approach is proposed to estimate the hub set when it is unknown. Supplementary materials for this article are available online.

Connected Hub Sets and Connected Hub Polynomials of the Lollipop Graph L_(p,1)

Connected Hub Sets and Connected Hub Polynomials of the Lollipop Graph L_(p,1)

Connectivity impairment of cerebellar and sensorimotor connector hubs in Parkinson's disease.

Cognitive and movement processes involved integration of several large-scale brain networks. Central to these integrative processes are connector hubs, brain regions characterized by strong connections with multiple networks. Growing evidence suggests that many neurodegenerative and psychiatric disorders are associated with connector hub dysfunctions. Using a network metric called functional connectivity overlap ratio, we investigated connector hub alterations in Parkinson’s disease. Resting-state functional MRI data from 99 patients (male/female = 44/55) and 99 age- and sex-matched healthy controls (male/female = 39/60) participating in our cross-sectional study were used in the analysis. We have identified two sets of connector hubs, mainly located in the sensorimotor cortex and cerebellum, with significant connectivity alterations with multiple resting-state networks. Sensorimotor connector hubs have impaired connections primarily with primary processing (sensorimotor, visual), visuospatial, and basal ganglia networks, whereas cerebellar connector hubs have impaired connections with basal ganglia and executive control networks. These connectivity alterations correlated with patients’ motor symptoms. Specifically, values of the functional connectivity overlap ratio of the cerebellar connector hubs were associated with tremor score, whereas that of the sensorimotor connector hubs with postural instability and gait disturbance score, suggesting potential association of each set of connector hubs with the disorder’s two predominant forms, the akinesia/rigidity and resting tremor subtypes. In addition, values of the functional connectivity overlap ratio of the sensorimotor connector hubs were highly predictive in classifying patients from controls with an accuracy of 75.76%. These findings suggest that, together with the basal ganglia, cerebellar and sensorimotor connector hubs are significantly involved in Parkinson’s disease with their connectivity dysfunction potentially driving the clinical manifestations typically observed in this disorder.

Integrated aviation model and metaheuristic algorithm for hub-and-spoke network design and airline fleet planning

Most extant studies on aviation’s hub-and-spoke network focus on the optimization problems from the perspective of economies of scale thanks to the inherent inter-hub connections, whereas the economies of scale on each route is contingent on the associated allocation of different types of aircraft. In this paper, we devise an integrated model of hub-and-spoke network design and fleet planning with the constraints of the passenger flow demand as well as the adaptation of different types of aircraft for each route, with the objective to minimize the total system cost (i.e., the sum of the hub setting cost and aircraft’s related cost). To tackle the complexity of the integrated model, we develop a heuristic solution algorithm based on a Genetic Algorithm framework and an improved Floyd–Warshall algorithm to solve the value of the fitness function. The proposed model and the developed algorithms are tested with the US flight network (i.e., the CAB dataset) and China’s aviation network. The sensitivity analysis reveals that the demand window is one of the critical factors that affect hub-and-spoke network design and fleet planning, while the utilization rate of specified aircraft fleet determines the fleet configuration, which poses relatively less impact on hub-and-spoke network design.

Functional Significance of Human Resting-State Networks Hubs Identified Using MEG During the Transition From Childhood to Adulthood.

Cortical hubs identified within resting-state networks (RSNs), areas of the cortex that have a higher-than-average number of connections, are known to be critical to typical cognitive functioning and are often implicated in disorders leading to abnormal cognitive functioning. Functionally defined cortical hubs are also known to change with age in the developing, maturing brain, mostly based on studies carried out using fMRI. We have recently used magnetoencephalography (MEG) to study the maturation trajectories of RSNs and their hubs from age 7 to 29 in 131 healthy participants with high temporal resolution. We found that maturation trajectories diverge as a function of the underlying cortical rhythm. Specifically, we found the beta band (13–30 Hz)-mediated RSNs became more locally efficient with maturation, i.e., more organized into clusters and connected with nearby regions, while gamma (31–80 Hz)-mediated RSNs became more globally efficient with maturation, i.e., prioritizing faster signal transmission between distant cortical regions. We also found that different sets of hubs were associated with each of these networks. To better understand the functional significance of this divergence, we wanted to examine the cortical functions associated with the identified hubs that grew or shrunk with maturation within each of these networks. To that end, we analyzed the results of the prior study using Neurosynth, a platform for large-scale, automated synthesis of fMRI data that links brain coordinates with their probabilistically associated terms. By mapping the Neurosynth terms associated with each of these hubs, we found that maturing hubs identified in the gamma band RSNs were more likely to be associated with bottom-up processes while maturing hubs identified in the beta band RSNs were more likely to be associated with top-down functions. The results were consistent with the idea that beta band-mediated networks preferentially support the maturation of top-down processing, while the gamma band-mediated networks preferentially support the maturation of bottom-up processing.

Allocation of Regional Logistics Hubs and Assessing Their Contribution to Saudi Arabia’s Logistics Performance Index Ranking

In 2016, Saudi Arabia published its vision for the year 2030, which is based on the Kingdom’s geographical, financial, social and religious potentials. Developing the logistics sector and improving the Kingdom’s ranking in the World Bank’s Logistics Performance Index (LPI) to the twenty-fifth rank was one of the most targeted success factors. Unfortunately, Saudi Arabia’s rank in this index has declined over the past years, until it reached the fifty-fifth rank as per the last published LPI report. This research proposes a set of logistics hubs (LHs) which are located at key multi-logistics areas within the regional trading zones of the country. A spatial model was implemented on a macro level to integrate multi-logistical, infrastructural and natural geographic information system (GIS) layers, and highlight their intersections as initial feasible areas. Subsequently, an optimization model based on integer linear programming (ILP) was used to maximize the number of allocated LHs and minimize the overall distances between allocated LHs and international trading nodes considering multiple factors. More than 80 selected subject matter experts (SMEs) from 9 different countries have participated in World Bank’s driven surveys that assess the contribution of the LHs’ allocation proposal on Saudi Arabia’s LPI ranking. An improvement of about 10% in the LPI overall score with a 20 rank promotion has been estimated as per the SMEs responses. These results demonstrate that investing in logistics infrastructure and ranking in LPI are perfectly, positively and highly related.

Comprehensive 3D epigenomic maps define limbal stem/progenitor cell function and identity

The insights into how genome topology couples with epigenetic states to govern the function and identity of the corneal epithelium are poorly understood. Here, we generate a high-resolution Hi-C interaction map of human limbal stem/progenitor cells (LSCs) and show that chromatin multi-hierarchical organisation is coupled to gene expression. By integrating Hi-C, epigenome and transcriptome data, we characterize the comprehensive 3D epigenomic landscapes of LSCs. We find that super-silencers mediate gene repression associated with corneal development, differentiation and disease via chromatin looping and/or proximity. Super-enhancer (SE) interaction analysis identified a set of SE interactive hubs that contribute to LSC-specific gene activation. These active and inactive element-anchored loop networks occur within the cohesin-occupied CTCF-CTCF loops. We further reveal a coordinated regulatory network of core transcription factors based on SE-promoter interactions. Our results provide detailed insights into the genome organization principle for epigenetic regulation of gene expression in stratified epithelia.